The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 24, 2019

Filed:

May. 06, 2016
Applicant:

Dexcom, Inc., San Diego, CA (US);

Inventors:

Peter C. Simpson, Cardiff, CA (US);

Robert J. Boock, Carlsbad, CA (US);

David DeRenzy, San Diego, CA (US);

Laura J. Dunn, San Diego, CA (US);

Matthew Lawrence Johnson, Encinitas, CA (US);

Katherine Yerre Koehler, Solana Beach, CA (US);

Apurv Ullas Kamath, San Diego, CA (US);

Andrew Attila Pal, San Diego, CA (US);

David Price, Carlsbad, CA (US);

Eli Reihman, San Diego, CA (US);

Mark Wu, San Diego, CA (US);

Assignee:

DexCom, Inc., San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G09B 19/00 (2006.01); G06F 19/00 (2018.01); A61B 5/145 (2006.01); A61B 5/11 (2006.01); A61B 5/1495 (2006.01); A61B 5/00 (2006.01); G16H 50/50 (2018.01); G16H 15/00 (2018.01);
U.S. Cl.
CPC ...
G09B 19/00 (2013.01); A61B 5/0022 (2013.01); A61B 5/0077 (2013.01); A61B 5/1112 (2013.01); A61B 5/1118 (2013.01); A61B 5/1495 (2013.01); A61B 5/14532 (2013.01); A61B 5/14546 (2013.01); A61B 5/486 (2013.01); A61B 5/4833 (2013.01); A61B 5/4866 (2013.01); A61B 5/4872 (2013.01); A61B 5/742 (2013.01); A61B 5/7435 (2013.01); A61B 5/7475 (2013.01); G06F 19/3456 (2013.01); G09B 19/0092 (2013.01); A61B 2562/0219 (2013.01); G06F 19/3475 (2013.01); G06F 19/3481 (2013.01); G16H 15/00 (2018.01); G16H 50/50 (2018.01); Y02A 90/26 (2018.01);
Abstract

Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.


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